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P N Suganthan - One of the best experts on this subject based on the ideXlab platform.

  • Analyzing Convergence Performance of evolutionary algorithms: A statistical approach
    Information Sciences, 2014
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, P N Suganthan, Francisco Herrera
    Abstract:

    The analysis of the Performance of different approaches is a staple concern in the design of Computational Intelligence experiments. Any proper analysis of evolutionary optimization algorithms should incorporate a full set of benchmark problems and state-of-the-art comparison algorithms. For the sake of rigor, such an analysis may be completed with the use of statistical procedures, supporting the conclusions drawn. In this paper, we point out that these conclusions are usually limited to the final results, whereas intermediate results are seldom considered. We propose a new methodology for comparing evolutionary algorithms’ Convergence capabilities, based on the use of Page’s trend test. The methodology is presented with a case of use, incorporating real results from selected techniques of a recent special issue. The possible applications of the method are highlighted, particularly in those cases in which the final results do not enable a clear evaluation of the differences among several evolutionary techniques.

  • statistical analysis of Convergence Performance throughout the evolutionary search a case study with sade mmts and sa epsde mmts
    2013 IEEE Symposium on Differential Evolution (SDE), 2013
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, Francisco Herrera, P N Suganthan
    Abstract:

    Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its Convergence Performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' Convergence Performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.

  • SDE - Statistical analysis of Convergence Performance throughout the evolutionary search: A case study with SaDE-MMTS and Sa-EPSDE-MMTS
    2013 IEEE Symposium on Differential Evolution (SDE), 2013
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, Francisco Herrera, P N Suganthan
    Abstract:

    Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its Convergence Performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' Convergence Performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.

  • IEEE Congress on Evolutionary Computation - Comprehensive comparison of Convergence Performance of optimization algorithms based on nonparametric statistical tests
    2012 IEEE Congress on Evolutionary Computation, 2012
    Co-Authors: Shizheng Zhao, P N Suganthan
    Abstract:

    In evolutionary computation, statistical tests are commonly used to improve the comparative evaluation process of the Performance of different algorithms. In this paper, three state-of-the-art Differential Evolution (DE) based algorithms, namely Dynamic Memetic Differential Evolution (MOS), Self-adaptive DE hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS) and Self-adaptive Differential Evolution Algorithm using Population Size Reduction and three Strategies Algorithm (jDElscop) as well as a novel algorithm called ensemble of parameters and mutation strategies in Differential Evolution with Self-adaption and MMTS (Sa-EPSDE-MMTS), are tested on the most recent LSO benchmark problems and comparatively evaluated using nonparametric statistical analysis. Instead of using the “Value-to-Reach” as the comparison criterion, comprehensive comparison over multiple evolution points are investigated on each test problem in order to quantitatively compare Convergence Performance of different algorithms. Our investigations demonstrate that even though all these algorithms yield the same final solutions on a large set of problems, they possess statistically significant variations during the Convergence. Hence, we propose that evolutionary algorithms can be compared statistically along the evolution paths.

Ryohei Nakano - One of the best experts on this subject based on the ideXlab platform.

  • Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach
    Lecture Notes in Computer Science, 2006
    Co-Authors: Kazumi Saito, Ryohei Nakano
    Abstract:

    We address the task of improving Convergence Performance of PageRank computation. Based on a step-length calculation approach, we derive three methods, which respectively calculates its step-length so as to make the successive search directions orthogonal (orthogonal direction), minimize the error at the next iteration (minimum error) and make the successive search directions conjugate (conjugate direction). In our experiments using a real Web network, we show that the minimum error method is promising for this task.

  • KES (2) - Improving Convergence Performance of pagerank computation based on step-length calculation approach
    Lecture Notes in Computer Science, 2006
    Co-Authors: Kazumi Saito, Ryohei Nakano
    Abstract:

    We address the task of improving Convergence Performance of PageRank computation. Based on a step-length calculation approach, we derive three methods, which respectively calculates its step-length so as to make the successive search directions orthogonal (orthogonal direction), minimize the error at the next iteration (minimum error) and make the successive search directions conjugate (conjugate direction). In our experiments using a real Web network, we show that the minimum error method is promising for this task.

Kazumi Saito - One of the best experts on this subject based on the ideXlab platform.

  • Improving Convergence Performance of PageRank Computation Based on Step-Length Calculation Approach
    Lecture Notes in Computer Science, 2006
    Co-Authors: Kazumi Saito, Ryohei Nakano
    Abstract:

    We address the task of improving Convergence Performance of PageRank computation. Based on a step-length calculation approach, we derive three methods, which respectively calculates its step-length so as to make the successive search directions orthogonal (orthogonal direction), minimize the error at the next iteration (minimum error) and make the successive search directions conjugate (conjugate direction). In our experiments using a real Web network, we show that the minimum error method is promising for this task.

  • KES (2) - Improving Convergence Performance of pagerank computation based on step-length calculation approach
    Lecture Notes in Computer Science, 2006
    Co-Authors: Kazumi Saito, Ryohei Nakano
    Abstract:

    We address the task of improving Convergence Performance of PageRank computation. Based on a step-length calculation approach, we derive three methods, which respectively calculates its step-length so as to make the successive search directions orthogonal (orthogonal direction), minimize the error at the next iteration (minimum error) and make the successive search directions conjugate (conjugate direction). In our experiments using a real Web network, we show that the minimum error method is promising for this task.

Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.

  • Analyzing Convergence Performance of evolutionary algorithms: A statistical approach
    Information Sciences, 2014
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, P N Suganthan, Francisco Herrera
    Abstract:

    The analysis of the Performance of different approaches is a staple concern in the design of Computational Intelligence experiments. Any proper analysis of evolutionary optimization algorithms should incorporate a full set of benchmark problems and state-of-the-art comparison algorithms. For the sake of rigor, such an analysis may be completed with the use of statistical procedures, supporting the conclusions drawn. In this paper, we point out that these conclusions are usually limited to the final results, whereas intermediate results are seldom considered. We propose a new methodology for comparing evolutionary algorithms’ Convergence capabilities, based on the use of Page’s trend test. The methodology is presented with a case of use, incorporating real results from selected techniques of a recent special issue. The possible applications of the method are highlighted, particularly in those cases in which the final results do not enable a clear evaluation of the differences among several evolutionary techniques.

  • statistical analysis of Convergence Performance throughout the evolutionary search a case study with sade mmts and sa epsde mmts
    2013 IEEE Symposium on Differential Evolution (SDE), 2013
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, Francisco Herrera, P N Suganthan
    Abstract:

    Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its Convergence Performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' Convergence Performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.

  • SDE - Statistical analysis of Convergence Performance throughout the evolutionary search: A case study with SaDE-MMTS and Sa-EPSDE-MMTS
    2013 IEEE Symposium on Differential Evolution (SDE), 2013
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, Francisco Herrera, P N Suganthan
    Abstract:

    Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its Convergence Performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' Convergence Performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.

Joaquin Derrac - One of the best experts on this subject based on the ideXlab platform.

  • Analyzing Convergence Performance of evolutionary algorithms: A statistical approach
    Information Sciences, 2014
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, P N Suganthan, Francisco Herrera
    Abstract:

    The analysis of the Performance of different approaches is a staple concern in the design of Computational Intelligence experiments. Any proper analysis of evolutionary optimization algorithms should incorporate a full set of benchmark problems and state-of-the-art comparison algorithms. For the sake of rigor, such an analysis may be completed with the use of statistical procedures, supporting the conclusions drawn. In this paper, we point out that these conclusions are usually limited to the final results, whereas intermediate results are seldom considered. We propose a new methodology for comparing evolutionary algorithms’ Convergence capabilities, based on the use of Page’s trend test. The methodology is presented with a case of use, incorporating real results from selected techniques of a recent special issue. The possible applications of the method are highlighted, particularly in those cases in which the final results do not enable a clear evaluation of the differences among several evolutionary techniques.

  • statistical analysis of Convergence Performance throughout the evolutionary search a case study with sade mmts and sa epsde mmts
    2013 IEEE Symposium on Differential Evolution (SDE), 2013
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, Francisco Herrera, P N Suganthan
    Abstract:

    Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its Convergence Performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' Convergence Performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.

  • SDE - Statistical analysis of Convergence Performance throughout the evolutionary search: A case study with SaDE-MMTS and Sa-EPSDE-MMTS
    2013 IEEE Symposium on Differential Evolution (SDE), 2013
    Co-Authors: Joaquin Derrac, Salvador Garcia, Sheldon Hui, Francisco Herrera, P N Suganthan
    Abstract:

    Typically, comparisons among optimization algorithms only considers the results obtained at the end of the search process. However, there are occasions in which is very interesting to perform comparisons along the search. This way, algorithms could also be categorized depending on its Convergence Performance, which would help when deciding which algorithms perform better among a set of methods that are assumed as equal when only the results at the end of the search are considered. In this work, we present a procedure to perform a pairwise comparison of two algorithms' Convergence Performance. A non-parametric procedure, the Page test, is used to detect significant differences between the evolution of the error of the algorithms as the search continues. A case of study has been also provided to demonstrate the application of the test.